Algorithms with More Granular Differential Privacy Guarantees

Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parameters have been proposed.

In this work, we consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis.

In this framework, we study several basic data analysis and learning tasks, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person (i.e., all the attributes).